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A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms

Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algo...

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Main Authors: Ghazvini, Anahita, Mohd Sharef, Nurfadhlina, Balasundram, Siva Kumar, Lee, Lai Soon
Format: Article
Language:English
Published: MDPI 2024
Online Access:http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf
http://psasir.upm.edu.my/id/eprint/111545/
https://www.mdpi.com/2076-3417/14/8/3383
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spelling my.upm.eprints.1115452024-10-17T00:58:31Z http://psasir.upm.edu.my/id/eprint/111545/ A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms Ghazvini, Anahita Mohd Sharef, Nurfadhlina Balasundram, Siva Kumar Lee, Lai Soon Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. MDPI 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf Ghazvini, Anahita and Mohd Sharef, Nurfadhlina and Balasundram, Siva Kumar and Lee, Lai Soon (2024) A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms. Applied Sciences, 14 (8). art. no. 3383. pp. 1-24. ISSN 2076-3417 https://www.mdpi.com/2076-3417/14/8/3383 10.3390/app14083383
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system.
format Article
author Ghazvini, Anahita
Mohd Sharef, Nurfadhlina
Balasundram, Siva Kumar
Lee, Lai Soon
spellingShingle Ghazvini, Anahita
Mohd Sharef, Nurfadhlina
Balasundram, Siva Kumar
Lee, Lai Soon
A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
author_facet Ghazvini, Anahita
Mohd Sharef, Nurfadhlina
Balasundram, Siva Kumar
Lee, Lai Soon
author_sort Ghazvini, Anahita
title A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
title_short A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
title_full A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
title_fullStr A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
title_full_unstemmed A concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
title_sort concentration prediction-based crop digital twin using nutrient co-existence and composition in regression algorithms
publisher MDPI
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/111545/1/applsci-2754333.pdf
http://psasir.upm.edu.my/id/eprint/111545/
https://www.mdpi.com/2076-3417/14/8/3383
_version_ 1814054732652609536
score 13.211869

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